


'''simple docstring'''
import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=7							,      a_=True							,      a_=True							,      a_=False							,      a_=True							,      a_=9_9							,      a_=3_2							,      a_=5							,      a_=4							,      a_=3_7							,      a_="gelu"							,      a_=0.1							,      a_=0.1							,      a_=5_1_2							,      a_=1_6							,      a_=2							,      a_=0.02							,      a_=3							,      a_=4							,      a_=None							,      )    ->  Tuple:
					lowercase						:		Union[str, Any]   					=		parent
					lowercase						:		int   					=		batch_size
					lowercase						:		int   					=		seq_length
					lowercase						:		int   					=		is_training
					lowercase						:		List[Any]   					=		use_input_mask
					lowercase						:		str   					=		use_token_type_ids
					lowercase						:		List[str]   					=		use_labels
					lowercase						:		List[Any]   					=		vocab_size
					lowercase						:		Dict   					=		hidden_size
					lowercase						:		List[Any]   					=		num_hidden_layers
					lowercase						:		str   					=		num_attention_heads
					lowercase						:		str   					=		intermediate_size
					lowercase						:		Optional[int]   					=		hidden_act
					lowercase						:		Optional[int]   					=		hidden_dropout_prob
					lowercase						:		int   					=		attention_probs_dropout_prob
					lowercase						:		List[str]   					=		max_position_embeddings
					lowercase						:		Optional[Any]   					=		type_vocab_size
					lowercase						:		Any   					=		type_sequence_label_size
					lowercase						:		List[Any]   					=		initializer_range
					lowercase						:		List[str]   					=		num_labels
					lowercase						:		Union[str, Any]   					=		num_choices
					lowercase						:		Dict   					=		scope

				def 	a__		(     self							)    ->  Any:
					lowercase						:		Dict   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size							)

					lowercase						:		Union[str, Any]   					=		None
					if self.use_input_mask:
						lowercase						:		Optional[int]   					=		random_attention_mask([self.batch_size, self.seq_length]							)

					lowercase						:		List[Any]   					=		None
					if self.use_token_type_ids:
						lowercase						:		Any   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.type_vocab_size							)

					lowercase						:		Union[str, Any]   					=		None
					lowercase						:		Dict   					=		None
					lowercase						:		Any   					=		None
					if self.use_labels:
						lowercase						:		Tuple   					=		ids_tensor([self.batch_size]							,      self.type_sequence_label_size							)
						lowercase						:		Optional[int]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.num_labels							)
						lowercase						:		int   					=		ids_tensor([self.batch_size]							,      self.num_choices							)

					lowercase						:		Optional[int]   					=		self.get_config()

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

				def 	a__		(     self							)    ->  Any:
					return OpenLlamaConfig(
					    vocab_size=self.vocab_size							,      hidden_size=self.hidden_size							,      num_hidden_layers=self.num_hidden_layers							,      num_attention_heads=self.num_attention_heads							,      intermediate_size=self.intermediate_size							,      hidden_act=self.hidden_act							,      hidden_dropout_prob=self.hidden_dropout_prob							,      attention_probs_dropout_prob=self.attention_probs_dropout_prob							,      max_position_embeddings=self.max_position_embeddings							,      type_vocab_size=self.type_vocab_size							,      is_decoder=_lowercase							,      initializer_range=self.initializer_range							,      use_stable_embedding=_lowercase							,      )

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							)    ->  Dict:
					lowercase						:		Optional[Any]   					=		OpenLlamaModel(config=_lowercase							)
					model.to(_lowercase							)
					model.eval()
					lowercase						:		List[Any]   					=		model(_lowercase							,      attention_mask=_lowercase							)
					lowercase						:		Optional[Any]   					=		model(_lowercase							)
					self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      )    ->  Union[str, Any]:
					lowercase						:		int   					=		True
					lowercase						:		Union[str, Any]   					=		OpenLlamaModel(_lowercase							)
					model.to(_lowercase							)
					model.eval()
					lowercase						:		Optional[int]   					=		model(
					    _lowercase							,      attention_mask=_lowercase							,      encoder_hidden_states=_lowercase							,      encoder_attention_mask=_lowercase							,      )
					lowercase						:		Any   					=		model(
					    _lowercase							,      attention_mask=_lowercase							,      encoder_hidden_states=_lowercase							,      )
					lowercase						:		Optional[int]   					=		model(_lowercase							,      attention_mask=_lowercase							)
					self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      )    ->  Any:
					lowercase						:		Dict   					=		OpenLlamaForCausalLM(config=_lowercase							)
					model.to(_lowercase							)
					model.eval()
					lowercase						:		int   					=		model(_lowercase							,      attention_mask=_lowercase							,      labels=_lowercase							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.seq_length, self.vocab_size)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      )    ->  List[str]:
					lowercase						:		Dict   					=		True
					lowercase						:		Optional[int]   					=		True
					lowercase						:		Dict   					=		OpenLlamaForCausalLM(config=_lowercase							)
					model.to(_lowercase							)
					model.eval()

					# first forward pass
					lowercase						:		List[str]   					=		model(
					    _lowercase							,      attention_mask=_lowercase							,      encoder_hidden_states=_lowercase							,      encoder_attention_mask=_lowercase							,      use_cache=_lowercase							,      )
					lowercase						:		Optional[Any]   					=		outputs.past_key_values

					# create hypothetical multiple next token and extent to next_input_ids
					lowercase						:		Optional[Any]   					=		ids_tensor((self.batch_size, 3)							,      config.vocab_size							)
					lowercase						:		Union[str, Any]   					=		ids_tensor((self.batch_size, 3)							,      vocab_size=2							)

					# append to next input_ids and
					lowercase						:		Any   					=		torch.cat([input_ids, next_tokens]							,      dim=-1							)
					lowercase						:		Any   					=		torch.cat([input_mask, next_mask]							,      dim=-1							)

					lowercase						:		Optional[int]   					=		model(
					    _lowercase							,      attention_mask=_lowercase							,      encoder_hidden_states=_lowercase							,      encoder_attention_mask=_lowercase							,      output_hidden_states=_lowercase							,      )["""hidden_states"""][0]
					lowercase						:		Dict   					=		model(
					    _lowercase							,      attention_mask=_lowercase							,      encoder_hidden_states=_lowercase							,      encoder_attention_mask=_lowercase							,      past_key_values=_lowercase							,      output_hidden_states=_lowercase							,      )["""hidden_states"""][0]

					# select random slice
					lowercase						:		Optional[int]   					=		ids_tensor((1,)							,      output_from_past.shape[-1]							).item()
					lowercase						:		Dict   					=		output_from_no_past[:, -3:, random_slice_idx].detach()
					lowercase						:		Optional[int]   					=		output_from_past[:, :, random_slice_idx].detach()

					self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(_lowercase							,      _lowercase							,      atol=1e-3							)							)


				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		Any   					=		self.prepare_config_and_inputs()
					(
					    lowercase					
					)						:		Union[str, Any]   					=		config_and_inputs
					lowercase						:		Tuple   					=		{"""input_ids""": input_ids, """attention_mask""": input_mask}
					return config, inputs_dict




@require_torch
class        _UpperCamelCase	(    a_						,			a_						,			a_						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(
				    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
				)
				_snake_case						       =				(OpenLlamaForCausalLM,) if is_torch_available() else ()
				_snake_case						       =				(
				    {
				        '''feature-extraction''': OpenLlamaModel,
				        '''text-classification''': OpenLlamaForSequenceClassification,
				        '''text-generation''': OpenLlamaForCausalLM,
				        '''zero-shot''': OpenLlamaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				_snake_case						       =				False
				_snake_case						       =				False

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Optional[int]   					=		OpenLlamaModelTester(self							)
					lowercase						:		Optional[int]   					=		ConfigTester(self							,      config_class=_lowercase							,      hidden_size=3_7							)

				def 	a__		(     self							)    ->  Any:
					self.config_tester.run_common_tests()

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		Union[str, Any]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_lowercase							)

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		Any   					=		self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
						lowercase						:		Dict   					=		type
						self.model_tester.create_and_check_model(*_lowercase							)

				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		Tuple   					=		self.model_tester.prepare_config_and_inputs_for_common()
					lowercase						:		Any   					=		3
					lowercase						:		List[str]   					=		input_dict["""input_ids"""]
					lowercase						:		List[str]   					=		input_ids.ne(1							).to(_lowercase							)
					lowercase						:		int   					=		ids_tensor([self.model_tester.batch_size]							,      self.model_tester.type_sequence_label_size							)
					lowercase						:		int   					=		OpenLlamaForSequenceClassification(_lowercase							)
					model.to(_lowercase							)
					model.eval()
					lowercase						:		Tuple   					=		model(_lowercase							,      attention_mask=_lowercase							,      labels=_lowercase							)
					self.assertEqual(result.logits.shape							,      (self.model_tester.batch_size, self.model_tester.num_labels)							)

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		Any   					=		self.model_tester.prepare_config_and_inputs_for_common()
					lowercase						:		Union[str, Any]   					=		3
					lowercase						:		List[Any]   					=		"""single_label_classification"""
					lowercase						:		Dict   					=		input_dict["""input_ids"""]
					lowercase						:		int   					=		input_ids.ne(1							).to(_lowercase							)
					lowercase						:		List[str]   					=		ids_tensor([self.model_tester.batch_size]							,      self.model_tester.type_sequence_label_size							)
					lowercase						:		Any   					=		OpenLlamaForSequenceClassification(_lowercase							)
					model.to(_lowercase							)
					model.eval()
					lowercase						:		Any   					=		model(_lowercase							,      attention_mask=_lowercase							,      labels=_lowercase							)
					self.assertEqual(result.logits.shape							,      (self.model_tester.batch_size, self.model_tester.num_labels)							)

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		Union[str, Any]   					=		self.model_tester.prepare_config_and_inputs_for_common()
					lowercase						:		Any   					=		3
					lowercase						:		List[str]   					=		"""multi_label_classification"""
					lowercase						:		Dict   					=		input_dict["""input_ids"""]
					lowercase						:		Any   					=		input_ids.ne(1							).to(_lowercase							)
					lowercase						:		List[Any]   					=		ids_tensor(
					    [self.model_tester.batch_size, config.num_labels]							,      self.model_tester.type_sequence_label_size							).to(torch.float							)
					lowercase						:		Optional[int]   					=		OpenLlamaForSequenceClassification(_lowercase							)
					model.to(_lowercase							)
					model.eval()
					lowercase						:		int   					=		model(_lowercase							,      attention_mask=_lowercase							,      labels=_lowercase							)
					self.assertEqual(result.logits.shape							,      (self.model_tester.batch_size, self.model_tester.num_labels)							)

				@unittest.skip("Open-Llama buffers include complex numbers, which breaks this test"							)
				def 	a__		(     self							)    ->  Tuple:
					pass


				@parameterized.expand([("linear",), ("dynamic",)]							)
				def 	a__		(     self							,      a_							)    ->  str:
					lowercase						:		Optional[int]   					=		self.model_tester.prepare_config_and_inputs_for_common()
					lowercase						:		List[Any]   					=		ids_tensor([1, 1_0]							,      config.vocab_size							)
					lowercase						:		str   					=		ids_tensor([1, int(config.max_position_embeddings * 1.5							)]							,      config.vocab_size							)

					set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
					lowercase						:		Union[str, Any]   					=		OpenLlamaModel(_lowercase							)
					original_model.to(_lowercase							)
					original_model.eval()
					lowercase						:		Union[str, Any]   					=		original_model(_lowercase							).last_hidden_state
					lowercase						:		str   					=		original_model(_lowercase							).last_hidden_state

					set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
					lowercase						:		int   					=		{"""type""": scaling_type, """factor""": 1_0.0}
					lowercase						:		Union[str, Any]   					=		OpenLlamaModel(_lowercase							)
					scaled_model.to(_lowercase							)
					scaled_model.eval()
					lowercase						:		Optional[int]   					=		scaled_model(_lowercase							).last_hidden_state
					lowercase						:		Tuple   					=		scaled_model(_lowercase							).last_hidden_state

					# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
					# maximum sequence length, so the outputs for the short input should match.
					if scaling_type == "dynamic":
						self.assertTrue(torch.allclose(_lowercase							,      _lowercase							,      atol=1e-5							)							)
					else:
						self.assertFalse(torch.allclose(_lowercase							,      _lowercase							,      atol=1e-5							)							)

					# The output should be different for long inputs
					self.assertFalse(torch.allclose(_lowercase							,      _lowercase							,      atol=1e-5							)							)





from random import randint
from tempfile import TemporaryFile

import numpy as np


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						List[Any] 							= 0
			if start < end:
						a_						:						Dict 							= randint(a__  ,	a__)
						a_						:						List[str] 							= a[end]
						a_						:						Tuple 							= a[pivot]
						a_						:						Tuple 							= temp

						a_							,					a_						:						List[Any] 							= _in_place_partition(a__  ,	a__  ,	a__)
						count += _in_place_quick_sort(a__  ,	a__  ,	p - 1)
						count += _in_place_quick_sort(a__  ,	p + 1  ,	a__)
			return count


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= 0
			a_						:						Union[str, Any] 							= randint(a__  ,	a__)
			a_						:						Union[str, Any] 							= a[end]
			a_						:						Any 							= a[pivot]
			a_						:						Any 							= temp
			a_						:						int 							= start - 1
			for index in range(a__  ,	a__):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									a_						:						str 							= new_pivot_index + 1
									a_						:						Optional[Any] 							= a[new_pivot_index]
									a_						:						str 							= a[index]
									a_						:						Union[str, Any] 							= temp

			a_						:						Union[str, Any] 							= a[new_pivot_index + 1]
			a_						:						Tuple 							= a[end]
			a_						:						Any 							= temp
			return new_pivot_index + 1, count


__snake_case				:      Union[str, Any]   		=			TemporaryFile()
__snake_case				:      Dict   		=			1_00  # 1000 elements are to be sorted


__snake_case		, __snake_case				:      int   		=			0, 1  # mean and standard deviation
__snake_case				:      Tuple   		=			np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
__snake_case				:      str   		=			np.load(outfile)
__snake_case				:      Dict   		=			len(M) - 1
__snake_case				:      Dict   		=			_in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)



print(z)


